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Mastering Apache Storm

You're reading from   Mastering Apache Storm Real-time big data streaming using Kafka, Hbase and Redis

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781787125636
Length 284 pages
Edition 1st Edition
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Author (1):
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Ankit Jain Ankit Jain
Author Profile Icon Ankit Jain
Ankit Jain
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Toc

Table of Contents (13) Chapters Close

Preface 1. Real-Time Processing and Storm Introduction FREE CHAPTER 2. Storm Deployment, Topology Development, and Topology Options 3. Storm Parallelism and Data Partitioning 4. Trident Introduction 5. Trident Topology and Uses 6. Storm Scheduler 7. Monitoring of Storm Cluster 8. Integration of Storm and Kafka 9. Storm and Hadoop Integration 10. Storm Integration with Redis, Elasticsearch, and HBase 11. Apache Log Processing with Storm 12. Twitter Tweet Collection and Machine Learning

Custom scheduler

In Storm, Nimbus uses a scheduler to assign tasks to the supervisors. The default scheduler aims to allocate computing resources evenly to topologies. It works well in terms of fairness among topologies, but it is impossible for users to predict the placement of topology components in the Storm cluster, regarding which component of a topology needs to be assigned to which supervisor node.

Let's consider an example. Say that we have a topology that has one spout and two bolts, and each of the components has one executor and one task. The following diagram shows the distribution of the topology if we submit the topology to a Storm cluster. Assume that the number of workers assigned to the topology is three and the number of supervisors in the Storm cluster is three:

Let's assume that the last bolt in our topology, Bolt2, needs to process some data using...

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